3D shape completion is important to enable machines to perceive the complete
geometry of objects from partial observations. To address this problem,
view-based methods have been presented. These methods represent shapes as
multiple depth images, which can be back-projected to yield corresponding 3D
point clouds, and they perform shape completion by learning to complete each
depth image using neural networks. While view-based methods lead to
state-of-the-art results, they currently do not enforce geometric consistency
among the completed views during the inference stage. To resolve this issue, we
propose a multi-view consistent inference technique for 3D shape completion,
which we express as an energy minimization problem including a data term and a
regularization term. We formulate the regularization term as a consistency loss
that encourages geometric consistency among multiple views, while the data term
guarantees that the optimized views do not drift away too much from a learned
shape descriptor. Experimental results demonstrate that our method completes
shapes more accurately than previous techniques.

本论文提出了一种基于多视角的 3D 物体形状填充方法，通过学习使用神经网络完成每个深度图像，能够在保证不过度远离已学习形状描述符的前提下，在多个视角之间实现几何一致性，优于之前的完成技术。